By applying the T-Model to fingerprints, the determination for sufficiency to infer identification is no longer based on professional judgment (e.g., training, experience, educated conjecture, the unknown  workings of a "black box”, or “warm and fuzzy feelings”).  Although examiner professional judgment is needed to interpret ridge formation types and assess the quality of correspondence between ridge formations in two impressions, it no longer plays a part in the final determination of sufficiency or insufficiency to individualize.   As a result, final conclusions made using the T-Model may be considered completely objective.

In general, there are currently two basic, and different, approaches used by latent print examiners to establish sufficiency to infer identification: one is the empirical standard or numerical approach in which sufficiency is established based on a pre-determined minimum quantity of matching ridge features.  The other is a non-numerical approach that establishes sufficiency based on the quality of agreement between ridge formations in sequence, “which to make a decision whether the information in a particular case is sufficient, the expert must evaluate the clarity of the print, ascertain the quantity in agreement and the quality of the agreement. An opinion is then formed as to whether the prints are in agreement and if there is sufficient uniqueness to eliminate all other donors. This opinion is subjective and is based on the experience, knowledge and ability of the experts” [39]. 

Each approach may be considered either a pure non-numerical approach or a pure numerical approach which should not be confused with a statistical probability approach.  A statistical  probability approach applies quantitative weights and qualitative metrics to define numbers of look-alikes likely to occur in a given fingerprint population, fingerprint match probabilities and the estimated number of close matches or look-alikes likely to be present.  The T-Model is unique in this regard because it is essentially a combination of both a numerical (quantitative) and non-numerical (qualitative) approach to fingerprint identification that in the end utilizes probability theory and statistics to establish "sufficiency". What sets the T-Model apart of the above methods, is that it removes the decision-making process to make a fingerprint identification from the examiner.  It is the T-Model that declares a match, not the examiner.

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